Related Experiment Video
Updated: Jul 15, 2025
08:12
Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
9.5K
Using Deep Learning Models to Predict Prosthetic Ankle Torque
Christopher Prasanna1,2, Jonathan Realmuto3, Anthony Anderson1,2
1Center for Limb Loss and Mobility, Seattle, WA 98108, USA.
Sensors (Basel, Switzerland)
|September 28, 2023
Summary
Deep learning models accurately predict prosthetic ankle torque using minimal sensor data. This advancement enables more responsive and predictive control for powered prosthetic limbs, improving user experience.
Area of Science:
- Biomechanics
- Machine Learning
- Prosthetics
Background:
- Inverse dynamics via motion capture is standard for biomechanical data but is lab-bound and marker-intensive.
- A practical alternative is needed for real-time prosthetic control systems.
- Predictive controllers require rapid, accurate biomechanical information.
Purpose of the Study:
- To develop deep learning models for estimating and predicting prosthetic ankle torque.
- To utilize minimal input signals for accurate dynamical system modeling.
- To enable predictive control in high-bandwidth prosthesis systems.
Main Methods:
- Applied deep learning to create dynamical system models.
- Used a hyperparameter optimization protocol for automatic model selection.
- Trained deep neural networks on six input signals to predict ankle torque.
Main Results:
- Deep neural networks predicted future ankle torques with high accuracy (2.9 ± 1.6% RMSE).
- Compared to analytical models (26.6 ± 40.9% RMSE), deep learning showed superior performance.
- Future predictions (half gait cycle) showed minimal performance degradation (1.7% RMSE increase).
Conclusions:
- Deep learning offers a viable method for accurate prosthetic ankle torque prediction.
- This approach reduces reliance on complex motion capture setups.
- Enables development of advanced predictive control for powered prosthetic limbs.
